Modern reservoir simulation models include very detailed description of geology to enable accurate physics solutions. In order to increase the reliability of the simulation forecast, simulation models grew in size to a billion or more grid blocks, which poses a challenging complexity to build, validate, and history match the models. To help assess and handle new complexity and inherent uncertainties, an artificial intelligence algorithm based on Self-Organizing Maps (SOM) has been developed to explore and identify geologic model components based on similarities and dissimilarities in the model. Sector models can be generated based on these region definitions for focused region assessment, history matching, production/injection optimization, or optimal well configuration.
In this work, an unsupervised artificial intelligence neural network algorithm is devised that can combine both static properties distribution (permeability, Reservoir Quality Index (RQI), or porosity) and dynamic properties (pressures or saturations) to construct a similarities matrix. Standard deviation-based chaos minimization is then applied to merge similar regions (contiguous), identify similar but distant regions, and reduce the number of identified heterogeneous regions to the desired controlled number. The algorithm was applied on a synthetic reservoir model using the GigaPOWERS® simulator, The developed SOM-based algorithm conditions geology to reservoir dynamics, and reduce model computational requirements of assisted history matching (AHM) with minimal engineering effort.